R/effective_rlmc.R

In sl4bayesmeta: Sensitivity and learning for Bayesian meta-analysis

####---- effective Relative Latent Model Complexity computation ----####

effective_rlmc<-function(df,
                          r.tau.prior,
                          MM=10^6,
                          output="sample",
                          step=ifelse(output=="prob", 0.03, NULL)){
  # computation of the effective relative latent model complexity by MC sampling
  # input:
  # df: data frame containing a column df$sigma
  # r.tau.prior: randomisation function for the prior
  # MM: number of MC samples
  # output: "sample", "summary", "prob"
  # step: step value for bins for "prob"
  # results:
  # descriptive statistics or sample for the effective MRLMC
  # sample: sample
  # summary: descriptive statistics: summary: min, 0.25, 0.5, mean, 0.75, max
  # prob: a data frame with x,y columns for plotting
  
  
  set.seed(12567)
  
  tau_sim<-r.tau.prior(MM)
  kk<-length(df$sigma) # number od studies in the data frame
  pdsum<-0
  for (i in 1:kk){
    sim_ICCi<-tau_sim^2/(tau_sim^2+df$sigma[i]^2)
    pdsum<-pdsum+sim_ICCi
  }
  if(output=="sample"){
    return(pdsum/kk)
  }
  if(output=="summary"){
    return(summary(pdsum/kk))
  }
  if(output=="prob"){
    data<-pdsum/kk
    breaks <- seq(0,1,by=step)
    bin <- cut(data, breaks, include.lowest = TRUE)
    est <- tabulate(bin, length(levels(bin)))
    y <- est/(diff(breaks)*length(data))
    x<-breaks[-1]-step/2
    return(data.frame(x=x, y=y))
  }
}